Shebin Jose Jacob / Predictive Maintenance With Sound Public

Shebin Jose Jacob / Predictive Maintenance With Sound

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

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About this project

Predictive Maintenance With Sound

Every manufacturing environment is equipped with machines. For a better-performing manufacturing unit, the health of machines plays a major role and hence maintenance does. We have three strategies of maintenance namely - Preventive maintenance, Corrective maintenance, and Predictive maintenance.

If you want to find the best balance between preventing failures and avoiding over-maintenance, PdM is the way to go. Equip your factory with relatively affordable sensors to track temperature, vibrations, and motion data, use predictive techniques to schedule maintenance when a failure is about to occur, and you'll see a nice reduction in operating costs.

In the newest era of technology, teaching computers to make sense of the acoustic world is now a hot research topic. So in this project, we use sound to do some predictive maintenance with Nano 33 BLE Sense.


How Does It Work ?

We use Nano 33 BLE Sense to listen to the machine continuously. The MCU runs an ML model which is trained on two sets of acoustic anomalies and a normal operation mode. When the ML model identifies an anomaly, the operator is immediately notified and the machine may be shut down for maintenance after proper inspection. Thus we can reduce the possible damage caused and can reduce the downtime.

Hardware Requirements

  • Nano 33 BLE Sense
  • LED

Software Requirements

  • Edge Impulse
  • Arduino IDE

Hardware Setup

The hardware setup consists of a Nano 33 BLE Sense, which is placed beside an old AC motor.


Nano 33 BLE Sense.JPG

Software Setup

If you haven't connected the device to Edge Impulse dashboard, follow this tutorial to get it connected. After a successful connection, it should be present in the Devices tab.


Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. Follow this tutorial to learn more about it.

TinyML Model Generation

After successfully setting up hardware and software, now we are ready for the model generation which includes a number of steps. Let's start by collecting some data.

1. Data Collection

Clean data is the most important requirement to train a well-performing model. In our case, we have collected 3 classes of sound - two classes of anomalies, one normal operation class, and obviously a noise class. Each sample is 2 seconds long. The raw data of these classes is visualised below.


Anomaly 1.png

Anomaly 2.png


If the data is not split into training and testing datasets, split the dataset into training and testing datasets in the ratio 80:20, which forms a good dataset for model training.

2. Impulse Design

An impulse is the machine learning pipeline that takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.


Here we are using the Time Series data as the input block. Now, we have two choices for the processing block - MFCC and MFE. As we are dealing with non-vocal audio and MFE performs well with non-vocal audio, we have chosen MFE as our processing block. We have used Classification as our learning block since we have to learn patterns and apply them to new data to categorize the audio into one of the given 4 classes.


In the MFE tab, you can tweak the parameters if you're good with audio handling, else leave the settings as it is and generate features.


3. Model Training And Testing

Now that we have our impulse designed, let's proceed to train the model. The settings we employed for model training are depicted in the picture. You can play about with the model training settings so that the trained model exhibits a higher level of accuracy, but be cautious of overfitting.


A whopping 94.7% accuracy is achieved by the trained model.

Training Accuracy.png

Let's now use some unknown data to test the model's functionality. To assess the model's performance, move on to Model Testing and Classify All.

Testing Accuracy.png

We have got 95.07% testing accuracy, which is pretty awesome. Now let's test the model with some real-world data. Navigate to Live Classification and collect some data from the connected device.


Anomaly 1.jpg

Anomaly 2.jpg

We have collected some real-world data of Normal Operation Mode, Anomaly 1, and Anomaly 2 respectively, and all of them are correctly classified. So our model is ready for deployment.


For deployment, navigate to the Deployment tab, select Arduino Library and build the library. It will output a zip library, which can be added to Arduino IDE.



The entire assets for this project are given in this github repository.

Final Product

Nano 33 BLE Sense along with an LED is enclosed in a 3D printed case, which is our final product. The device is capable of identifying acoustic anomalies in a machine and alerts the user using the alert LED.



Download block output

Title Type Size
MFE training data NPY file 2826 windows
MFE training labels NPY file 2826 windows
MFE testing data NPY file 742 windows
MFE testing labels NPY file 742 windows
Classifier model TensorFlow Lite (float32) 15 KB
Classifier model TensorFlow Lite (int8 quantized) 8 KB
Classifier model TensorFlow SavedModel 19 KB
Classifier model Keras h5 model 14 KB

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Data collected
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Project info

Project ID 162492
Project version 1
License Apache 2.0